At its most basic, productivity is the amount of value produced divided by the amount of cost (or time) required to do so. And while this equation seems simple enough on the surface, the strategies for optimizing it have evolved dramatically over the last two decades. Technology has enabled massive personal productivity gains — computers, spreadsheets, email, and other advances have made it possible for a knowledge worker to seemingly produce more in a day then was previously possible in a year. It’s tempting to conclude that, if individuals are able to perform their work much better and faster, overall productivity must be soaring.

And yet there’s a problem. U.S. government data suggests overall labor productivity has only grown 1-2% per year during the tech boom. With trillions invested during this time period, that’s a hard number to reconcile. My strong hypothesis is that we’re focusing on the wrong kind of productivity — and, in turn, the wrong kind of management. It turns out that enterprise productivity is different than just the sum of personal productivity. This difference matters. A lot. And an example from my company, VoloMetrix (now part of Microsoft), can help illustrate exactly how.

We recently worked with a multi-billion dollar technology firm, where the majority of the company’s revenue comes through a large ecosystem of partners (e.g., resellers, manufacturers, etc.). They have enjoyed strong growth for many years, but recently made the decision to put more emphasis on growing profitably rather than just growing. One of the things that they wanted to understand was the cost of managing their partner ecosystem – they had a hypothesis that there might be ways to do so more efficiently.

They began by providing us a list of around 700 employees that they believed represented the population of partner-facing roles across their organization. They asked us to confirm that these employees were indeed partner-facing, and to let them know if they missed anyone. (For a bit of background context, we performed data mining on anonymized email and calendar header data, in combination with HR and customer relationship management data, to build a robust factbase of just how much time is spent in direct communication with each partner, by each team, across the entire organization, among other things.)

It turns out they were a bit off on the employee population involved. In reality, around7,000 employees directly interacted with partners for at least one hour per week over the course of a year. Roughly 2,000,000 hours of time were spent in these direct partner interactions (emails, meetings). This equates to approximately $200M of employee time per year, which doesn’t even include any of the internal discussions or preparations.

This is a big number. Worse, it’s an order of magnitude bigger then what company management had expected. In reality, they had no clue how their employees were collectively spending their time with regard to one of their single most important revenue-generating activities.

That said, big isn’t necessarily bad given how much revenue comes through their partners. However, we were then able to look for correlations between time invested in each partner and that partner’s success. We looked at growth, total bookings, strategic value and other outcome measures segmented by geography, partner type and length of relationship. Using some natural language processing we were also able to derive a decent estimate of the topics of each interaction (e.g., sales-related, product-related, program-related, etc.). The hope was that the time and cost invested in each partner paid dividends.